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config.json
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config.json
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{
"_desc_num_parties": "Number of parties involved in the MPC computation. Should match compiled application protocol.",
"num_parties": 3,
"_desc_party_ips": "IP addresses of each party, ordered by party number",
"party_ips": [],
"_desc_party_users": "Usernames for each party, ordered by party number. Can be used by scripts to SSH into machines if necessary to start piranha processes",
"party_users": [],
"_desc_run_unit_tests": "Run unit tests",
"run_unit_tests": false,
"_desc_unit_test_only": "Only run unit tests, do not try to run any full Piranha applications",
"unit_test_only": false,
"_desc_debug_print": "Show debug output",
"debug_print": false,
"_desc_debug_overflow": "Test for overflow and print related debug output. Breaks security by revealing intermediary values.",
"debug_overflow": false,
"_desc_debug_sqrt": "Test sqrt for invalid input and print related debug output. Breaks security by revealing intermediary values.",
"debug_sqrt": false,
"_desc_run_name": "Descriptive name for run, used to name log files with something useful",
"run_name": "hello_world_run",
"_desc_network": "Path to NN architecture to use for the run",
"network": "files/models/secureml-norelu.json",
"_desc_custom_epochs": "Enable custom number of epochs. Otherwise set by size of learning rate schedule",
"custom_epochs": false,
"_desc_custom_epoch_count": "Number of epochs to train for, if custom_epochs is set",
"custom_epoch_count": 10,
"_desc_custom_iterations": "Enable custom number of iterations. Otherwise set by training dataset size.",
"custom_iterations": false,
"_desc_custom_iteration_count": "Number of iterators to train per epoch, if custom_iterations is set",
"custom_iteration_count": 0,
"_desc_custom_batch_size": "Enable custom batch size. Otherwise set by architecture configuration.",
"custom_batch_size": true,
"_desc_custom_batch_size_count": "Desired custom batch size",
"custom_batch_size_count": 128,
"_desc_nn_seed": "Seed for NN initialization",
"nn_seed": 343934585,
"_desc_preload": "Preload weights from a snapshot directory instead of training from scratch",
"preload": false,
"_desc_preload_path": "Directory path from which to preload network weights",
"preload_path": "",
"_desc_lr_schedule": "Learning rate schedule, in negative powers of 2 (e.g. 3 -> learning rate of 2^-3). Assumes that the number of LR exponents matches the desired number of training epochs",
"lr_schedule": [3, 3, 3, 4, 4, 5, 6, 7, 8, 9],
"_desc_test_only": "Only run NN test, skip training (useful if weights have been preloaded)",
"test_only": false,
"_desc_inference_only": "Only run inference (forward pass), not backward pass training",
"inference_only": false,
"_desc_no_test": "Do not run testing after training epochs",
"no_test": false,
"_desc_last_test": "Only run a test pass after the last training epoch",
"last_test": false,
"_desc_iteration_snapshots": "Take snapshots at each training iteration",
"iteration_snapshots": false,
"_desc_test_iteration_snapshots": "Take snapshots of a '1PC' test network running the same data",
"test_iteration_snapshots": false,
"_desc_epoch_snapshots": "Take snapshots after each training epoch",
"epoch_snapshots": false,
"_desc_eval_accuracy": "Evaluation: print training/test accuracy",
"eval_accuracy": true,
"_desc_eval_inference_stats": "Evaluation: print runtime and communication statistics for each inference forward pass",
"eval_inference_stats": false,
"_desc_eval_train_stats": "Evaluation: print runtime and communication statistics for each training forward-backward pass",
"eval_train_stats": false,
"_desc_eval_fw_peak_memory": "Evaluation: print peak memory usage during each forward pass",
"eval_fw_peak_memory": false,
"_desc_eval_bw_peak_memory": "Evaluation: print peak memory usage during each backward pass",
"eval_bw_peak_memory": false,
"_desc_eval_epoch_stats": "Evaluation: print cumulative runtime and communication statistics for each training epoch",
"eval_epoch_stats": true,
"_desc_print_activations": "Print output activations for each layer every forward pass",
"print_activations": false,
"_desc_print_deltas": "Print input gradient to each layer every backward pass",
"print_deltas": false,
"_desc_debug_all_forward": "Print debug information for all layer forward passes",
"debug_all_forward": false,
"_desc_debug_all_backward": "Print debug information for all layer backward passes",
"debug_all_backward": false
}